MeetSecuritain—Built by Cloudain for Modern Cloud Securitysecuritain.com
We understand how frustrating it is when your organization's knowledge is scattered across documents, websites, repositories, and databases, out of reach when people need it most. That's why we're here: to help you make that knowledge usable. We help you connect generative AI to your own information using retrieval-augmented generation, which gives the model relevant content from approved sources at the moment a question is asked—so answers are more useful, current, and controlled.
RAG combines information retrieval with generative AI: understand the request, search approved sources, retrieve relevant content, give it to the model, and answer with supporting citations. A production RAG system is far more than a chatbot on a vector database—Cloudain designs the complete knowledge and retrieval architecture.
From enterprise and customer assistants to policy, technical, document research, and reusable knowledge services.
Secure internal assistants that surface information across approved organizational sources.
External-facing assistants with clear boundaries so they stay within approved content.
Help authorized users find and interpret policies, controls, and supporting evidence.
AI-assisted search across engineering, architecture, and operational documentation.
Work across large document collections with cross-document search and comparison.
Reusable retrieval and knowledge APIs—not only a standalone assistant.
An assistant must not reveal information simply because it exists in the index. Cloudain implements access-aware retrieval based on identity, role, department, tenant, and document classification—enforced through the application and retrieval architecture, not by asking the model to keep information private.
Not every source should be included—Cloudain helps determine what is authoritative, useful, current, and appropriate for AI retrieval.
Identify where relevant knowledge exists, who owns it, and how it is used and updated.
Assess suitability—outdated, duplicate, conflicting, or poorly structured content.
Extraction, OCR, cleaning, classification, metadata, and sensitive-data identification.
Divide documents into retrievable sections without losing meaning—by type and structure.
Keyword, semantic, vector, hybrid, reranking, and metadata filtering by question type.
Answer only from approved sources, show references, and state when info is unavailable.
Scheduled and event-driven updates, version replacement, and freshness monitoring.
Retrieval improves quality but doesn’t eliminate inaccuracy—the system should say it lacks information rather than generate a confident but unsupported answer.
Connected according to environment and permissions—curated for what is authoritative and current.
Ingestion, processing, storage & search, retrieval, generation, application, and operations—each with clear controls.
Source connectors, uploads, scheduled and event-driven sync, validation, and queues.
Text/structure extraction, classification, metadata enrichment, chunking, and embeddings.
Object storage, search index, vector database, metadata store, and graph relationships.
Query understanding, hybrid search, permission filtering, reranking, and context assembly.
Model selection, prompt management, output validation, citations, guardrails, and fallback.
Chat, employee portal, customer app, API, mobile, business workflow, and agent tool.
Logging, monitoring, usage analytics, cost tracking, evaluation, and access review.
Selected by data location, scale, security, integration, and cost—across major clouds and portable open technologies.
Designed to support information access and workflows—not to replace clinical, legal, or regulatory judgment.
Final scope depends on the number and quality of sources, access complexity, integrations, users, and risk of incorrect answers.
From a readiness assessment and a focused pilot to an enterprise assistant or a shared retrieval platform.
Identify sources, ownership, quality, access restrictions, and preparation requirements.
A controlled assistant for one business area, user group, or approved collection.
A secure experience across multiple repositories, departments, and user roles.
Reusable ingestion, indexing, search, and retrieval for many assistants and agents.
Assess retrieval quality, grounding, access controls, cost, latency, and readiness.
Improve source content, metadata, ownership, and governance for enterprise search.
Value depends on the workflow it improves—compared with an agreed baseline after users, sources, and success criteria are defined.
Cloudain combines generative AI, enterprise search, data integration, cloud architecture, and security to make organizational knowledge easier to find and apply—grounded in approved information and designed for production use.
Answers across every source
Backed by approved content
Only what the user may see
